Various models have been proposed to perform reasoning with spatiotemporal data, including Bayesian networks and neural networks. Bayesian networks provide a well-established way to represent causal relationships using a structure. But they are typically designed by humans with expertise in the problem domain, and can suffer from human error, ideologies, preconceived notions, and prejudices. The use of Bayesian networks can therefore produce inaccurate and incomplete representations of the problem domain.
Bayesian networks also tend to require extensive human involvement through design and training. This can make them very expensive to implement. Dynamic Bayesian belief networks, such as such as Hidden Markov Models, have also been proposed (see, e.g., “An introduction to hidden Markov models,” by L. R. Rabiner and B. H. Juang, IEEE ASSP Mag., pp 4-16, June 1986.). But their development is even more complex, and is still fundamentally based on human effort.
Neural networks are computational systems that use interconnected modeled neurons, which may mimic cognitive or biological functions. These networks can be trained to process information for different types of problems. But they tend to exhibit “black box” characteristics, and the structure of the selected model generally cannot be used for causal analysis.